Introduction Parkinson’s Disease is the second most prevalent neurodegenerative disorder after Alzheimer’s, affecting more than 10 million people worldwide. Parkinson’s is characterized primarily by the deterioration of motor and cognitive ability. There is no single test which can be administered for diagnosis. Instead, doctors must perform a careful clinical analysis of the patient’s medical history. Unfortunately, this method of diagnosis is highly inaccurate. A study from the National Institute of Neurological Disorders finds that early diagnosis (having symptoms for 5 years or less) is only 53% accurate. This is not much better than random guessing, but an early diagnosis is critical to effective treatment. Because of these difficulties, I investigate a machine learning approach to accurately diagnose Parkinson’s, using a dataset of various speech features (a non-invasive yet characteristic tool) from the University of Oxford. Why speech features? Speech is very predictive and characteristic of Parkinson’s disease; almost every Parkinson’s patient experiences severe vocal degradation (inability to produce sustained phonations, tremor, hoarseness), so it makes sense to use voice to diagnose the disease. Voice analysis gives the added benefit of being non-invasive, inexpensive, and very easy to extract clinically. Background Parkinson's Disease Parkinson’s is a progressive neurodegenerative condition resulting from the death of the dopamine containing cells of the substantia nigra (which plays an important role in movement). Symptoms include: “frozen” facial features, bradykinesia (slowness of movement), akinesia (impairment of voluntary movement), tremor, and voice impairment. Typically, by the time the disease is diagnosed, 60% of nigrostriatal neurons have degenerated, and 80% of striatal dopamine have been depleted. Performance Metrics TP = true positive, FP = false positive, TN = true negative, FN = false negative Accuracy: (TP+TN)/(P+N) Matthews Correlation Coefficient: 1=perfect, 0=random, -1=completely inaccurate Algorithms Employed Logistic Regression (LR): Uses the sigmoid logistic equation with weights (coefficient values) and biases (constants) to model the probability of a certain class for binary classification. An output of 1 represents one class, and an output of 0 represents the other. Training the model will learn the optimal weights and biases. Linear Discriminant Analysis (LDA): Assumes that the data is Gaussian and each feature has the same variance. LDA estimates the mean and variance for each class from the training data, and then uses properties of statistics (Bayes theorem , Gaussian distribution, etc) to compute the probability of a particular instance belonging to a given class. The class with the largest probability is the prediction. k Nearest Neighbors (KNN): Makes predictions about the validation set using the entire training set. KNN makes a prediction about a new instance by searching through the entire set to find the k “closest” instances. “Closeness” is determined using a proximity measurement (Euclidean) across all features. The class that the majority of the k closest instances belong to is the class that the model predicts the new instance to be. Decision Tree (DT): Represented by a binary tree, where each root node represents an input variable and a split point, and each leaf node contains an output used to make a prediction. Neural Network (NN): Models the way the human brain makes decisions. Each neuron takes in 1+ inputs, and then uses an activation function to process the input with weights and biases to produce an output. Neurons can be arranged into layers, and multiple layers can form a network to model complex decisions. Training the network involves using the training instances to optimize the weights and biases. Naive Bayes (NB): Simplifies the calculation of probabilities by assuming that all features are independent of one another (a strong but effective assumption). Employs Bayes Theorem to calculate the probabilities that the instance to be predicted is in each class, then finds the class with the highest probability. Gradient Boost (GB): Generally used when seeking a model with very high predictive performance. Used to reduce bias and variance (“error”) by combining multiple “weak learners” (not very good models) to create a “strong learner” (high performance model). Involves 3 elements: a loss function (error function) to be optimized, a weak learner (decision tree) to make predictions, and an additive model to add trees to minimize the loss function. Gradient descent is used to minimize error after adding each tree (one by one). Engineering Goal Produce a machine learning model to diagnose Parkinson’s disease given various features of a patient’s speech with at least 90% accuracy and/or a Matthews Correlation Coefficient of at least 0.9. Compare various algorithms and parameters to determine the best model for predicting Parkinson’s. Dataset Description Source: the University of Oxford 195 instances (147 subjects with Parkinson’s, 48 without Parkinson’s) 22 features (elements that are possibly characteristic of Parkinson’s, such as frequency, pitch, amplitude / period of the sound wave) 1 label (1 for Parkinson’s, 0 for no Parkinson’s) Project Pipeline pipeline Summary of Procedure Split the Oxford Parkinson’s Dataset into two parts: one for training, one for validation (evaluate how well the model performs) Train each of the following algorithms with the training set: Logistic Regression, Linear Discriminant Analysis, k Nearest Neighbors, Decision Tree, Neural Network, Naive Bayes, Gradient Boost Evaluate results using the validation set Repeat for the following training set to validation set splits: 80% training / 20% validation, 75% / 25%, and 70% / 30% Repeat for a rescaled version of the dataset (scale all the numbers in the dataset to a range from 0 to 1: this helps to reduce the effect of outliers) Conduct 5 trials and average the results Data a_o a_r m_o m_r Data Analysis In general, the models tended to perform the best (both in terms of accuracy and Matthews Correlation Coefficient) on the rescaled dataset with a 75-25 train-test split. The two highest performing algorithms, k Nearest Neighbors and the Neural Network, both achieved an accuracy of 98%. The NN achieved a MCC of 0.96, while KNN achieved a MCC of 0.94. These figures outperform most existing literature and significantly outperform current methods of diagnosis. Conclusion and Significance These robust results suggest that a machine learning approach can indeed be implemented to significantly improve diagnosis methods of Parkinson’s disease. Given the necessity of early diagnosis for effective treatment, my machine learning models provide a very promising alternative to the current, rather ineffective method of diagnosis. Current methods of early diagnosis are only 53% accurate, while my machine learning model produces 98% accuracy. This 45% increase is critical because an accurate, early diagnosis is needed to effectively treat the disease. Typically, by the time the disease is diagnosed, 60% of nigrostriatal neurons have degenerated, and 80% of striatal dopamine have been depleted. With an earlier diagnosis, much of this degradation could have been slowed or treated. My results are very significant because Parkinson’s affects over 10 million people worldwide who could benefit greatly from an early, accurate diagnosis. Not only is my machine learning approach more accurate in terms of diagnostic accuracy, it is also more scalable, less expensive, and therefore more accessible to people who might not have access to established medical facilities and professionals. The diagnosis is also much simpler, requiring only a 10-15 second voice recording and producing an immediate diagnosis. Future Research Given more time and resources, I would investigate the following: Create a mobile application which would allow the user to record his/her voice, extract the necessary vocal features, and feed it into my machine learning model to diagnose Parkinson’s. Use larger datasets in conjunction with the University of Oxford dataset. Tune and improve my models even further to achieve even better results. Investigate different structures and types of neural networks. Construct a novel algorithm specifically suited for the prediction of Parkinson’s. Generalize my findings and algorithms for all types of dementia disorders, such as Alzheimer’s. References Bind, Shubham. "A Survey of Machine Learning Based Approaches for Parkinson Disease Prediction." International Journal of Computer Science and Information Technologies 6 (2015): n. pag. International Journal of Computer Science and Information Technologies. 2015. Web. 8 Mar. 2017. Brooks, Megan. "Diagnosing Parkinson's Disease Still Challenging." Medscape Medical News. National Institute of Neurological Disorders, 31 July 2014. Web. 20 Mar. 2017. Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection', Little MA, McSharry PE, Roberts SJ, Costello DAE, Moroz IM. BioMedical Engineering OnLine 2007, 6:23 (26 June 2007) Hashmi, Sumaiya F. "A Machine Learning Approach to Diagnosis of Parkinson’s Disease."Claremont Colleges Scholarship. Claremont College, 2013. Web. 10 Mar. 2017. Karplus, Abraham. "Machine Learning Algorithms for Cancer Diagnosis." Machine Learning Algorithms for Cancer Diagnosis (n.d.): n. pag. Mar. 2012. Web. 20 Mar. 2017. Little, Max. "Parkinsons Data Set." UCI Machine Learning Repository. University of Oxford, 26 June 2008. Web. 20 Feb. 2017. Ozcift, Akin, and Arif Gulten. "Classifier Ensemble Construction with Rotation Forest to Improve Medical Diagnosis Performance of Machine Learning Algorithms." Computer Methods and Programs in Biomedicine 104.3 (2011): 443-51. Semantic Scholar. 2011. Web. 15 Mar. 2017. "Parkinson’s Disease Dementia." UCI MIND. N.p., 19 Oct. 2015. Web. 17 Feb. 2017. Salvatore, C., A. Cerasa, I. Castiglioni, F. Gallivanone, A. Augimeri, M. Lopez, G. Arabia, M. Morelli, M.c. Gilardi, and A. Quattrone. "Machine Learning on Brain MRI Data for Differential Diagnosis of Parkinson's Disease and Progressive Supranuclear Palsy."Journal of Neuroscience Methods 222 (2014): 230-37. 2014. Web. 18 Mar. 2017. Shahbakhi, Mohammad, Danial Taheri Far, and Ehsan Tahami. "Speech Analysis for Diagnosis of Parkinson’s Disease Using Genetic Algorithm and Support Vector Machine."Journal of Biomedical Science and Engineering 07.04 (2014): 147-56. Scientific Research. July 2014. Web. 2 Mar. 2017. "Speech and Communication." Speech and Communication. Parkinson's Disease Foundation, n.d. Web. 22 Mar. 2017. Sriram, Tarigoppula V. S., M. Venkateswara Rao, G. V. Satya Narayana, and D. S. V. G. K. Kaladhar. "Diagnosis of Parkinson Disease Using Machine Learning and Data Mining Systems from Voice Dataset." SpringerLink. Springer, Cham, 01 Jan. 1970. Web. 17 Mar. 2017.

Performing Leaf Image classification for Recognition of Plant Diseases using various types of CNN Architecture, For detection of Diseased Leaf and thus helping the increase in crop yield.

Agricultural productivity is something on which economy highly depends. This is the one of the reasons that disease detection in plants plays an important role in agriculture field, as having disease in plants are quite natural. If proper care is not taken in this area then it causes serious effects on plants and due to which respective product quality, quantity or productivity is affected. For instance a disease named little leaf disease is a hazardous disease found in pine trees in United States. Detection of plant disease through some automatic technique is beneficial as it reduces a large work of monitoring in big farms of crops, and at very early stage itself it detects the symptoms of diseases i.e. when they appear on plant leaves. This paper introduces an efficient approach to identify healthy and diseased or an infected leaf using image processing and machine learning techniques. Various diseases damage the chlorophyll of leaves and affect with brown or black marks on the leaf area. These can be detected using image prepossessing, image segmentation. Support Vector Machine (SVM) is one of the machine learning algorithms is used for classification. The Convolutional Neural Network (CNN) resulted in a improved accuracy of recognition compared to the SVM approach.

A project to train and evaluate different dnn models for plant disease detection problem, tackle the problem of scarce real-life representative data, experiment with different generative networks and generate more plant leaf image data and implement segmentation pipeline to avoid miss-classification due to unwanted input

A large-scale dataset for classification and detection of apple leaf diseases

Flask App Which detects 15 variety of plants [Pepper , Potato , Tomato ]

The project is based on the leaf disease detection using cnn model and provide remedies for the disease plants.

Coffee Leaf Classification and Disease Detection using YOLO-v11. A thesis project.

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Performing Leaf Image classification for Recognition of Plant Diseases using various types of CNN Architecture, For detection of Diseased Leaf and thus helping the increase in crop yield.

This project predicts diseases affecting plant leaves. It offers early detection and intervention for farmers and gardeners, utilizing image classification algorithms to identify various leaf diseases.

Identification of diseases from the images of a tomato leaf is one of the interesting research areas in the agriculture field, for which machine learning concepts of computer field can be applied. My research presents a prototype system for detection and classification of tomato leaf diseases based on the images of infected tomato leaf. We consider 10 tomato diseases named Bacterial_spot, Early_blight, late_blight, Leaf_Mold, Septoria_leaf_spot, Spider_mites Two-spotted_spider_mite, Target_Spot, Tomato_Yellow_Leaf_Curl_Virus, Tomato_mosaic_virus, healthy. It can also detect Healthy leafs. In this research, I used deep learning based model (CNN) for classification. First, I pre-processed the image dataset very carefully because preprocessing is the most important part of this research. Then I trained my model and validate according to the dataset. I test various techniques for this research but faster rcnn works pretty well for my dataset, it gives an accuracy level of 89%. If there is no image of the tomato leaf then it can also be detected.

Diseases in the leaves of plants are very crucial issue in this day and due to which yield of high-quality crops gets devasted, and the longevity of the plant hampers. And also, it is very difficult to understand the current condition of the leaf with the naked eye. Which results in the reduction of yield of high-quality crops. To overcome this problem, we planned to use machine learning based approach to segment, to select every small part of the leaf and detect the disease, also to analyse the quality. The main vision of the paper is to detect all possible diseases of the leaves of the plant by applying Neural Network for classify the disease based on the colour changes in comparison to the analytical available data set, providing the best fit output. In this proposed work, Apple plant leaf dataset used which contain 1910 images of healthy and unhealthy leaves. These leaves are pre-processed first through some steps. In this pre-processing convert the RGB image with the help of BGR2GRAY function available on open cv library then GaussianBlur function is used to remove the additional noises and smoothing of the edges of the leaves which helped to detect the main object or leaf more accurately from the image in future. After smoothing threshold function is applied for removing the unnecessary background from the image especially thresh_torezo_inv from cv2 library used for this work it helped to convert the unnecessary background into a single colour and focus on the main object i.e. leaf. After thresholding Erode and Dilate functions are applied for more cleaner image. Then with the help of FindContours() function from cv2 library helped to find four extreme points of the desired object i.e. leaf and crop the image according to the extreme points. Then the images are resized into 240, 240px size, it is necessary all the images are in same size for the best result output. Then each image is transformed to an n-dimensional array and appended into a list. After this pre-processing the data set is splitted into 3 different parts, i.e. training, validation and testing. 1337 images are used for training i.e. training part contains 1337 images, 287 images are used for validation and 286 images are used for testing. After splitting the dataset, the main convolutional neural network model is constructed. In the convolutional neural network model one input layer used and two hidden layers used and one out put layer used. In the hidden layer 32 nodes each are used with normal weight initialization and ReLu function for Activation. And in the output layer 1 node is used and sigmoid function for activation. On the compilation of the model stochastic gradient descent used for optimizing with a learning rate 0.05 and loss being calculated using categorical_crossentropy. After compilation of the model it is fitted into the training dataset with batch size of 32 and 10 epochs.

Detection of Leaf Disease with Deep Learning using Convolutional Neural Network for image classification and detection

A comprehensive dataset of rice leaf images for disease classification tasks, designed for agricultural computer vision applications focusing on rice plant health monitoring and disease detection.

Developed a plant disease detection system using deep learning for early disease identification. Compared VGG16 and ResNet50 models on a dataset of healthy and diseased leaf images. ResNet50 achieved 90.24% test accuracy, outperforming VGG16. Improved crop management through advanced image classification.

Plant Disease Detection Using CNN Deep Learning Project A deep learning–based system that automatically detects diseases in plant leaves using image classification. This project uses a Convolutional Neural Network to classify leaf images into healthy or diseased categories, helping farmers and researchers identify crop diseases at an early stage.

This dataset contains labeled images of tomato plant leaves affected by various diseases, including early blight, late blight, and leaf mold, as well as healthy leaves. It is intended for use in training and evaluating machine learning models for plant disease detection and classification.

A mobile and ML project for plant analysis, including disease detection, species classification, and leaf/age analysis. Provides Python backend for model training and inference, and a React Native mobile app for on-device usage.

This project aims developing system that can identify and categorize diseases in plant leaves from images. This process integrates various stages from data collection to deployment, utilizing advanced machine learning techniques to improve agricultural productivity and plant health.

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In this project, I intend to build classification model for the detection of the type of plant and its disease form the image of the diseased leaf.

MATLAB-based solution for rice disease classification using CNNs. Enables efficient data processing, model training, and evaluation for diagnosing bacterial leaf blight, leaf smut, and brown spot diseases in rice crops. Streamlined interface facilitates easy deployment and customization for accurate agricultural disease detection.

This project uses CNNs for early detection and classification of rice leaf diseases. It analyzes leaf images to identify diseases and recommends eco-friendly pesticides for sustainable crop management. The solution aids in proactive disease management, reducing pesticide use, and promoting environmental safety.

This project aims to develop a CNN-based system for early detection and classification of plant diseases using leaf images to support precision agriculture.

Deep learning-based Corn Disease Detection using a ResNeXt model. The model is trained on augmented corn leaf images with various diseases, achieving high accuracy. Includes a custom ResNeXt architecture, confusion matrix, classification report, and random prediction visualization.

Developed a Machine Learning-based Android app for soybean disease detection. Trained CNN on leaf images and deployed using ONNX Runtime for real-time classification via camera/gallery. Provides farmers with instant, offline, and low-cost disease diagnosis to support smart farming.

The main aim of this project is to identify the diseases a plant leaf is suffering from so that we can give clear instruction to the farmer about the disease and the measures to be taken using CNN thereby reducing the economical losses. The Plant diseases effect the growth of the crop and reduces the quality of production. Convolutional neural networks helps in identification of features from the input images without the intervention of humans. Convolutional neural networks contains different layers and in each layer there are different activation function called neurons and have an impact on input image at each layer for the feature identification and disease detection. Based on the disease certain prevention measures are insisted to the farmers. Neural networks are used because of their great impact in the image classification.